Perceptron - ~b-cl-dec - logreg - {‘zipmap’: False}#

Fitted on a problem type ~b-cl-dec (see find_suitable_problem), method decision_function matches output . Model was converted with additional parameter: <class 'sklearn.linear_model._perceptron.Perceptron'>={'zipmap': False}.

Perceptron(n_jobs=8)

index

0

skl_nop

1

skl_ncoef

1

skl_nlin

1

onx_size

777

onx_nnodes

8

onx_ninits

5

onx_doc_string

onx_ir_version

8

onx_domain

ai.onnx

onx_model_version

0

onx_producer_name

skl2onnx

onx_producer_version

1.11.1

onx_

13

onx_ai.onnx.ml

1

onx_op_Cast

1

onx_op_Reshape

1

onx_size_optim

777

onx_nnodes_optim

8

onx_ninits_optim

5

fit_coef_.shape

(1, 4)

fit_intercept_.shape

1

fit_classes_.shape

2
%0 X X float((0, 4)) MatMul MatMul (MatMul) X->MatMul label label int64((0,)) probabilities probabilities float((0, 2)) ArgMax ArgMax (ArgMax) axis=1 probabilities->ArgMax classes classes int32((2,)) [0 1] ArrayFeatureExtractor ArrayFeatureExtractor (ArrayFeatureExtractor) classes->ArrayFeatureExtractor coef coef float32((4, 1)) [[-1.580544 ] [-6.4302855] [10.998623 ] [ 5.123207 ]] coef->MatMul intercept intercept float32((1,)) [-1.] Add Add (Add) intercept->Add negate negate float32(()) -1.0 Mul Mul (Mul) negate->Mul shape_tensor shape_tensor int64((1,)) [-1] Reshape Reshape (Reshape) shape_tensor->Reshape matmul_result matmul_result matmul_result->Add MatMul->matmul_result score score score->Mul Concat Concat (Concat) axis=1 score->Concat Add->score negated_scores negated_scores negated_scores->Concat Mul->negated_scores Concat->probabilities predicted_label predicted_label predicted_label->ArrayFeatureExtractor ArgMax->predicted_label final_label final_label final_label->Reshape ArrayFeatureExtractor->final_label reshaped_final_label reshaped_final_label Cast Cast (Cast) to=7 reshaped_final_label->Cast Reshape->reshaped_final_label Cast->label

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